GASB 34 Reporting of the Value of Buried Infrastructure

Using Artificial Intelligence and Machine Learning to Assess and Monetize the Condition of Water Mains

By Doug Hatler & Greg Baird

Asset Management practices combined with the latest condition assessment tools using artificial intelligence, specifically machine learning, to assess the condition of buried water mains provides a new method for aligning maintenance, repair and replacement strategies to better allocate limited resources. Underground pipe performance evaluations can be established with an objective, data driven approach like machine learning and used to meet accounting’s GASB 34 Modified Approach requirement of a systemwide condition assessment three years. This greatly reduces the time required by accounting to report on buried infrastructure systems while increasing the accuracy and value of the financial statements.

Machine learning-based condition assessment tools are now commercially available. One example is Fracta. Fracta offers a fast, accurate and affordable digital condition assessment solution to predict the Likelihood of Failure (LoF) of water distribution mains. The accurate LOF score can then be coupled with Consequence of Failure (COF) to calculate a monetized Business Risk Exposure (BRE) and an estimated replacement cost for every buried water main in a distribution system.